MURAL - Maynooth University Research Archive Library

    UAV-mounted hyperspectral mapping of intertidal macroalgae

    Rossiter, Thomas and Furey, Thomas and McCarthy, Tim and Stengel, Dagmar B. (2020) UAV-mounted hyperspectral mapping of intertidal macroalgae. Estuarine, Coastal and Shelf Science, 242. p. 106789. ISSN 02727714

    [img] Download (102kB)

    Share your research

    Twitter Facebook LinkedIn GooglePlus Email more...

    Add this article to your Mendeley library


    Intertidal macroalgal communities mark the boundary of the marine realm and are faced with many direct and indirect anthropogenic pressures. The effective and sustainable management of these resources must be underpinned by accurate, efficient and cost-effective environmental data collection. Traditional field survey methods, whilst accurate, are time-consuming and limited in the area that can be covered. Remote sensing permits large areas to be rapidly surveyed but the effectiveness of satellites and aircraft for mapping fine-scale intertidal macroalgal mapping is limited by their coarse spatial resolution and restricted operational flexibility. The rapid development of unoccupied aerial vehicle (UAV) and sensor technology can address these issues and provide a potential alternative to established remote sensing platforms. Here, a detailed methodology is presented for the assessment of the commercially and ecologically important intertidal brown macroalga Ascophyllum nodosum using a multirotor UAV and pushbroom hyperspectral sensor. Two different classifiers, Maximum Likelihood Classifier (MLC) and Spectral Angle Mapper (SAM), were compared along with two different sources of spectral profiles, one collected in-situ with a spectral radiometer and the other derived from hyperspectral imagery. Of the classifiers compared, both trained using image-derived spectra, MLC more accurately classified A. nodosum, and other common intertidal species and substratum (Overall Accuracy (OA) 94.7%) than SAM (OA 81.1%). In addition, SAM, trained using in-situ spectra, was the least accurate of the three classifier workflows used (OA 71.4%). The low accuracy of the spectral radiometer approach was likely due to high levels of noise present in the hyperspectral data, a result of the relative instability of the UAV platform causing vibration. The accurate mapping of non-target species also highlights the applicability of this methodology for a broader range of intertidal macroalgal species and communities. This research clearly demonstrates the potential of UAV-mounted hyperspectral remote sensing for mapping the spatially and spectral complex macroalgal habitats found within the intertidal zone.

    Item Type: Article
    Keywords: Hyperspectral; Intertidal; Macroalgae; Ascophyllum nodosum; UAVs; Remote sensing;
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Faculty of Science and Engineering > Research Institutes > National Centre for Geocomputation, NCG
    Faculty of Social Sciences > Research Institutes > Maynooth University Social Sciences Institute, MUSSI
    Item ID: 16253
    Identification Number:
    Depositing User: Tim McCarthy
    Date Deposited: 06 Jul 2022 08:53
    Journal or Publication Title: Estuarine, Coastal and Shelf Science
    Refereed: Yes
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

    Repository Staff Only(login required)

    View Item Item control page


    Downloads per month over past year

    Origin of downloads